Opioids have become the cornerstone for treatment of moderate to severe acute and chronic pain of all types. Unfortunately, a group of maladaptations has been recognized as limiting the long-term effectiveness of these drugs including the phenomena of analgesic tolerance, opioid-induced hyperalgesia and physical dependence. Moreover, there has been a very sharp rise in the rate of opioid abuse paralleling the rise in their prescription for the treatment of pain. Our slow progress in uncovering the mechanisms responsible for opioid maladaptations and abuse has limited our ability to predict, prevent, and treat these problems. Genetic factors have been shown to influence the likelihood of abusing opioids as well as developing maladaptations both in humans and in rodent laboratory models. Understanding the basis for these effects would improve our understanding of the involved mechanisms and might suggest novel prevention or treatment strategies. To this point, however, progress in identifying specific genetic variants responsible for these effects has been limited. Whole genome haplotype based computational genetic mapping (HBCGM) offers a powerful approach to making novel genetic discoveries. In the proposed work we will overcome several key limitations to the use of HBCGM for exploring opioid abuse and in so doing will provide tools applicable to the analysis of a broad range of quantitative traits. Our fundamental goals are 1) to expand our murine whole genome sequence database to 30 widely available inbred strains thus providing the power to perform whole genome mapping, 2) expand our murine opioid response phenotype database to include strain- specific information for a range of clinically relevant opioid maladaptations, 3) to develop analytical tools helping investigators take advantage of the complex datasets generated in whole genome HBCGM experiments, and 4) to use our new sequence, phenotype and analytical tools to advance fundamentally our understanding of the genetic factors influencing responses to chronic opioid administration. Our group is uniquely qualified to undertake this tightly focused yet fundamentally important project. The research team includes investigators with expertise and experience in opioid pharmacology, genetics and computer science. Additional collaboration has been arranged with an expert on complementary integrative genomics approaches involving QTL results, expression data, and other strategies further enhancing the power of the project.